Features of the Fourth Edition include:New material on sample size calculations for chance-corrected agreement coefficients, as well as for intraclass correlation coefficients. The researcher will be able to determine the optimal number raters, subjects, and trials per subject.The chapter entitled “Benchmarking Inter-Rater Reliability Coefficients” has been entirely rewritten.The introductory chapter has been substantially expanded to explore possible definitions of the notion of inter-rater reliability.All chapters have been revised to a large extent to improve their readability.
". . . [this book] should be on the shelf of everyone interested in . . . longitudinal data analysis."
—Journal of the American Statistical Association
Features newly developed topics and applications of the analysis of longitudinal data
Applied Longitudinal Analysis, Second Edition presents modern methods for analyzing data from longitudinal studies and now features the latest state-of-the-art techniques. The book emphasizes practical, rather than theoretical, aspects of methods for the analysis of diverse types of longitudinal data that can be applied across various fields of study, from the health and medical sciences to the social and behavioral sciences.
The authors incorporate their extensive academic and research experience along with various updates that have been made in response to reader feedback. The Second Edition features six newly added chapters that explore topics currently evolving in the field, including:Fixed effects and mixed effects models Marginal models and generalized estimating equations Approximate methods for generalized linear mixed effects models Multiple imputation and inverse probability weighted methods Smoothing methods for longitudinal data Sample size and power
Each chapter presents methods in the setting of applications to data sets drawn from the health sciences. New problem sets have been added to many chapters, and a related website features sample programs and computer output using SAS, Stata, and R, as well as data sets and supplemental slides to facilitate a complete understanding of the material.
With its strong emphasis on multidisciplinary applications and the interpretation of results, Applied Longitudinal Analysis, Second Edition is an excellent book for courses on statistics in the health and medical sciences at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and professionals in the medical, public health, and pharmaceutical fields as well as those in social and behavioral sciences who would like to learn more about analyzing longitudinal data.
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.
New to the Third Edition
Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code
The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces.
The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.
“This book should be an essential part of the personal library of every practicing statistician.”—Technometrics
Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation.
Written by leading statisticians, Nonparametric Statistical Methods, Third Edition provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the Third Edition features:The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics Problems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science Nonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics.
Learn to evaluate and apply statistics in medicine, medical research, and all health-related fields.Emphasis on the basics of biostatistics and epidemiology and the clinical applications in evidence-based medicine and decision-making methods NEW chapter on survey research Expanded discussion of logistic regression, the Cox model, and other multivariate statistical methods Key Concepts in each chapter pinpoint essential information Presenting Problems drawn from studies in the medical literature that illustrate the various statistical methods Downloadable NCSS statistical software, procedures, and data sets from the presenting problems End-of-chapter exercises Multiple-choice final practice exam
This classroom-tested book covers the main subjects of a standard undergraduate probability course, including basic probability rules, standard models for describing collections of data, and the laws of large numbers. It also discusses several more advanced topics, such as the ballot theorem, the arcsine law, and random walks, as well as some specialized poker issues, such as the quantification of luck and skill in Texas Hold’em. Homework problems are provided at the end of each chapter.
The author includes examples of actual hands of Texas Hold’em from the World Series of Poker and other major tournaments and televised games. He also explains how to use R to simulate Texas Hold’em tournaments for student projects. R functions for running the tournaments are freely available from CRAN (in a package called holdem).
See Professor Schoenberg discuss the book.
Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.
The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses.
The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992).
From the reviews:
"This book provides a unified introduction to the regression methods listed in the title...The methods are well illustrated by data drawn from medical studies...A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered." Journal of Biopharmaceutical Statistics, 2005
"This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow...I heartily recommend the book" Technometrics, February 2006
"Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this well-unified approach should appeal to students who learn conceptually and verbally." Journal of the American Statistical Association, March 2006
In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.
Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and Hadoop
Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
“This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one’s personal library.”
—Journal of the American Statistical Association
Mixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. Mixed Models: Theory and Applications with R, Second Edition fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R.
The new edition provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing.
Mixed Models: Theory and Applications with R, Second Edition features unique applications of mixed model methodology, as well as:Comprehensive theoretical discussions illustrated by examples and figures Over 300 exercises, end-of-section problems, updated data sets, and R subroutines Problems and extended projects requiring simulations in R intended to reinforce material Summaries of major results and general points of discussion at the end of each chapter Open problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations
Ideal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.
The author concentrates on inferential procedures within the framework of parametric models, but - acknowledging that models are often incorrectly specified - he also views estimation from a non-parametric perspective. Overall, Mathematical Statistics places greater emphasis on frequentist methodology than on Bayesian, but claims no particular superiority for that approach. It does emphasize, however, the utility of statistical and mathematical software packages, and includes several sections addressing computational issues.
The result reaches beyond "nice" mathematics to provide a balanced, practical text that brings life and relevance to a subject so often perceived as irrelevant and dry.
This volume provides formulas and procedures for determination of sample size required not only for testing equality, but also for testing non-inferiority/superiority, and equivalence (similarity) based on both untransformed (raw) data and log-transformed data under a parallel-group design or a crossover design with equal or unequal ratio of treatment allocations. It contains a comprehensive and unified presentation of statistical procedures for sample size calculation that are commonly employed at various phases of clinical development. Each chapter includes, whenever possible, real examples of clinical studies from therapeutic areas such as cardiovascular, central nervous system, anti-infective, oncology, and women's health to demonstrate the clinical and statistical concepts, interpretations, and their relationships and interactions.
The book highlights statistical procedures for sample size calculation and justification that are commonly employed in clinical research and development. It provides clear, illustrated explanations of how the derived formulas and/or statistical procedures can be used.
• Introduces requisite background to using Nonlinear Mixed Effects Modeling (NONMEM), covering data requirements, model building and evaluation, and quality control aspects
• Provides examples of nonlinear modeling concepts and estimation basics with discussion on the model building process and applications of empirical Bayesian estimates in the drug development environment
• Includes detailed chapters on data set structure, developing control streams for modeling and simulation, model applications, interpretation of NONMEM output and results, and quality control
• Has datasets, programming code, and practice exercises with solutions, available on a supplementary website
The aim of this book is to show how R can be used as the software tool in the development of Six Sigma projects. The book includes a gentle introduction to Six Sigma and a variety of examples showing how to use R within real situations. It has been conceived as a self contained piece. Therefore, it is addressed not only to Six Sigma practitioners, but also to professionals trying to initiate themselves in this management methodology. The book may be used as a text book as well.
This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include:A chapter on the analysis of correlated outcome data A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion Detailed examples and interpretation of the presented results as well as exercises throughout
Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.
This new edition of Medical Statistics at a Glance:Presents key facts accompanied by clear and informative tables and diagrams Focuses on illustrative examples which show statistics in action, with an emphasis on the interpretation of computer data analysis rather than complex hand calculations Includes extensive cross-referencing, a comprehensive glossary of terms and flow-charts to make it easier to choose appropriate tests Now provides the learning objectives for each chapter Includes a new chapter on Developing Prognostic Scores Includes new or expanded material on study management, multi-centre studies, sequential trials, bias and different methods to remove confounding in observational studies, multiple comparisons, ROC curves and checking assumptions in a logistic regression analysis The companion website at www.medstatsaag.com contains supplementary material including an extensive reference list and multiple choice questions (MCQs) with interactive answers for self-assessment.
Medical Statistics at a Glance will appeal to all medical students, junior doctors and researchers in biomedical and pharmaceutical disciplines.
Reviews of the previous editions
"The more familiar I have become with this book, the more I appreciate the clear presentation and unthreatening prose. It is now a valuable companion to my formal statistics course."
–International Journal of Epidemiology
"I heartily recommend it, especially to first years, but it's equally appropriate for an intercalated BSc or Postgraduate research. If statistics give you headaches - buy it. If statistics are all you think about - buy it."
"...I unreservedly recommend this book to all medical students, especially those that dislike reading reams of text. This is one book that will not sit on your shelf collecting dust once you have graduated and will also function as a reference book."
–4th Year Medical Student, Barts and the London Chronicle, Spring 2003
The prediction of failures involves uncertainty, and problems associated with failures are inherently probabilistic. Their solution requires optimal tools to analyze strength of evidence and understand failure events and processes to gauge confidence in a design’s reliability.
Reliability Engineering and Risk Analysis: A Practical Guide, Second Edition has already introduced a generation of engineers to the practical methods and techniques used in reliability and risk studies applicable to numerous disciplines. Written for both practicing professionals and engineering students, this comprehensive overview of reliability and risk analysis techniques has been fully updated, expanded, and revised to meet current needs. It concentrates on reliability analysis of complex systems and their components and also presents basic risk analysis techniques. Since reliability analysis is a multi-disciplinary subject, the scope of this book applies to most engineering disciplines, and its content is primarily based on the materials used in undergraduate and graduate-level courses at the University of Maryland. This book has greatly benefited from its authors' industrial experience. It balances a mixture of basic theory and applications and presents a large number of examples to illustrate various technical subjects. A proven educational tool, this bestselling classic will serve anyone working on real-life failure analysis and prediction problems.
Includes practical examples from recent trials
Bringing together leading statisticians, scientists, and clinicians from the pharmaceutical industry, academia, and regulatory agencies, Multiple Testing Problems in Pharmaceutical Statistics explores the rapidly growing area of multiple comparison research with an emphasis on pharmaceutical applications. In each chapter, the expert contributors describe important multiplicity problems encountered in pre-clinical and clinical trial settings.
The book begins with a broad introduction from a regulatory perspective to different types of multiplicity problems that commonly arise in confirmatory controlled clinical trials, before giving an overview of the concepts, principles, and procedures of multiple testing. It then presents statistical methods for analyzing clinical dose response studies that compare several dose levels with a control as well as statistical methods for analyzing multiple endpoints in clinical trials. After covering gatekeeping procedures for testing hierarchically ordered hypotheses, the book discusses statistical approaches for the design and analysis of adaptive designs and related confirmatory hypothesis testing problems. The final chapter focuses on the design of pharmacogenomic studies based on established statistical principles. It also describes the analysis of data collected in these studies, taking into account the numerous multiplicity issues that occur.
This volume explains how to solve critical issues in multiple testing encountered in pre-clinical and clinical trial applications. It presents the necessary statistical methodology, along with examples and software code to show how to use the methods in practice.
"...a reference for everyone who is interested in knowing and handling uncertainty."
—Journal of Applied Statistics
The critically acclaimed First Edition of Understanding Uncertainty provided a study of uncertainty addressed to scholars in all fields, showing that uncertainty could be measured by probability, and that probability obeyed three basic rules that enabled uncertainty to be handled sensibly in everyday life. These ideas were extended to embrace the scientific method and to show how decisions, containing an uncertain element, could be rationally made.
Featuring new material, the Revised Edition remains the go-to guide for uncertainty and decision making, providing further applications at an accessible level including:A critical study of transitivity, a basic concept in probability A discussion of how the failure of the financial sector to use the proper approach to uncertainty may have contributed to the recent recession A consideration of betting, showing that a bookmaker's odds are not expressions of probability Applications of the book’s thesis to statistics A demonstration that some techniques currently popular in statistics, like significance tests, may be unsound, even seriously misleading, because they violate the rules of probability
Understanding Uncertainty, Revised Edition is ideal for students studying probability or statistics and for anyone interested in one of the most fascinating and vibrant fields of study in contemporary science and mathematics.
Collecting, analysing and drawing inferences from data is central to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the intended data. The literature on inference from the resulting incomplete data is now huge, and continues to grow both as methods are developed for large and complex data structures, and as increasing computer power and suitable software enable researchers to apply these methods.
This book focuses on a particular statistical method for analysing and drawing inferences from incomplete data, called Multiple Imputation (MI). MI is attractive because it is both practical and widely applicable. The authors aim is to clarify the issues raised by missing data, describing the rationale for MI, the relationship between the various imputation models and associated algorithms and its application to increasingly complex data structures.
Multiple Imputation and its Application:Discusses the issues raised by the analysis of partially observed data, and the assumptions on which analyses rest. Presents a practical guide to the issues to consider when analysing incomplete data from both observational studies and randomized trials. Provides a detailed discussion of the practical use of MI with real-world examples drawn from medical and social statistics. Explores handling non-linear relationships and interactions with multiple imputation, survival analysis, multilevel multiple imputation, sensitivity analysis via multiple imputation, using non-response weights with multiple imputation and doubly robust multiple imputation.
Multiple Imputation and its Application is aimed at quantitative researchers and students in the medical and social sciences with the aim of clarifying the issues raised by the analysis of incomplete data data, outlining the rationale for MI and describing how to consider and address the issues that arise in its application.
Basic Gambling Mathematics: The Numbers Behind the Neon explains the mathematics involved in analyzing games of chance, including casino games, horse racing, and lotteries. The book helps readers understand the mathematical reasons why some gambling games are better for the player than others. It is also suitable as a textbook for an introductory course on probability.
Along with discussing the mathematics of well-known casino games, the author examines game variations that have been proposed or used in actual casinos. Numerous examples illustrate the mathematical ideas in a range of casino games while end-of-chapter exercises go beyond routine calculations to give readers hands-on experience with casino-related computations.
The book begins with a brief historical introduction and mathematical preliminaries before developing the essential results and applications of elementary probability, including the important idea of mathematical expectation. The author then addresses probability questions arising from a variety of games, including roulette, craps, baccarat, blackjack, Caribbean stud poker, Royal Roulette, and sic bo. The final chapter explores the mathematics behind "get rich quick" schemes, such as the martingale and the Iron Cross, and shows how simple mathematics uncovers the flaws in these systems.
This Book:Surveys basic statistical methods used in the genetics and epidemiology literature, including maximum likelihood and least squares. Introduces methods, such as permutation testing and bootstrapping, that are becoming more widely used in both genetic and epidemiological research. Is illustrated throughout with simple examples to clarify the statistical methodology. Explains Bayes’ theorem pictorially. Features exercises, with answers to alternate questions, enabling use as a course text.
Written at an elementary mathematical level so that readers with high school mathematics will find the content accessible. Graduate students studying genetic epidemiology, researchers and practitioners from genetics, epidemiology, biology, medical research and statistics will find this an invaluable introduction to statistics.
* import and preprocessing of data from various sources
* statistical modeling of differential gene expression
* biological metadata
* application of graphs and graph rendering
* machine learning for clustering and classification problems
* gene set enrichment analysis
Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table.
New York Times Bestseller
“Not so different in spirit from the way public intellectuals like John Kenneth Galbraith once shaped discussions of economic policy and public figures like Walter Cronkite helped sway opinion on the Vietnam War…could turn out to be one of the more momentous books of the decade.”
—New York Times Book Review
"Nate Silver's The Signal and the Noise is The Soul of a New Machine for the 21st century."
—Rachel Maddow, author of Drift
"A serious treatise about the craft of prediction—without academic mathematics—cheerily aimed at lay readers. Silver's coverage is polymathic, ranging from poker and earthquakes to climate change and terrorism."
—New York Review of Books
Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of FiveThirtyEight.com.
Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.
In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science.
Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.
With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read.
From the Trade Paperback edition.
This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
The Essentials For Dummies Series
Dummies is proud to present our new series, The Essentials For Dummies. Now students who are prepping for exams, preparing to study new material, or who just need a refresher can have a concise, easy-to-understand review guide that covers an entire course by concentrating solely on the most important concepts. From algebra and chemistry to grammar and Spanish, our expert authors focus on the skills students most need to succeed in a subject.
Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start.Use your existing programming skills to learn and understand Bayesian statisticsWork with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testingGet started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockeyLearn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.
The fun and easy way to get down to business with statistics
Stymied by statistics? No fear? this friendly guide offers clear, practical explanations of statistical ideas, techniques, formulas, and calculations, with lots of examples that show you how these concepts apply to your everyday life.
Statistics For Dummies shows you how to interpret and critique graphs and charts, determine the odds with probability, guesstimate with confidence using confidence intervals, set up and carry out a hypothesis test, compute statistical formulas, and more.Tracks to a typical first semester statistics course Updated examples resonate with today's students Explanations mirror teaching methods and classroom protocol
Packed with practical advice and real-world problems, Statistics For Dummies gives you everything you need to analyze and interpret data for improved classroom or on-the-job performance.
This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as well as how it contrasts with the conventional approach. The theory is built up step by step, and important notions such as sufficiency are brought out of a discussion of the salient features of specific examples.
This edition:Includes expanded coverage of Gibbs sampling, including more numerical examples and treatments of OpenBUGS, R2WinBUGS and R2OpenBUGS. Presents significant new material on recent techniques such as Bayesian importance sampling, variational Bayes, Approximate Bayesian Computation (ABC) and Reversible Jump Markov Chain Monte Carlo (RJMCMC). Provides extensive examples throughout the book to complement the theory presented. Accompanied by a supporting website featuring new material and solutions.
More and more students are realizing that they need to learn Bayesian statistics to meet their academic and professional goals. This book is best suited for use as a main text in courses on Bayesian statistics for third and fourth year undergraduates and postgraduate students.
For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
Several survey data sets are used to illustrate how to design samples, to make estimates from complex surveys for use in optimizing the sample allocation, and to calculate weights. Realistic survey projects are used to demonstrate the challenges and provide a context for the solutions. The book covers several topics that either are not included or are dealt with in a limited way in other texts. These areas include: sample size computations for multistage designs; power calculations related to surveys; mathematical programming for sample allocation in a multi-criteria optimization setting; nuts and bolts of area probability sampling; multiphase designs; quality control of survey operations; and statistical software for survey sampling and estimation. An associated R package, PracTools, contains a number of specialized functions for sample size and other calculations. The data sets used in the book are also available in PracTools, so that the reader may replicate the examples or perform further analyses.
Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles.
The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including:
Markov Chain Monte Carlo algorithms in Bayesian inference
Generalized linear models
Bayesian hierarchical models
Predictive distribution and model checking
Bayesian model and variable evaluation
Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book's related Web site.
Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.
Sampling of Populations, Fourth Edition continues to serve as an all-inclusive resource on the basic and most current practices in population sampling. Maintaining the clear and accessible style of the previous edition, this book outlines the essential statistical methodsfor survey design and analysis, while also exploring techniques that have developed over the past decade.
The Fourth Edition successfully guides the reader through the basic concepts and procedures that accompany real-world sample surveys, such as sampling designs, problems of missing data, statistical analysis of multistage sampling data, and nonresponse and poststratification adjustment procedures. Rather than employ a heavily mathematical approach, the authors present illustrative examples that demonstrate the rationale behind common steps in the sampling process, from creating effective surveys to analyzing collected data. Along with established methods, modern topics are treated through the book's new features, which include:A new chapter on telephone sampling, with coverage of declining response rates, the creation of "do not call" lists, and the growing use of cellular phones A new chapter on sample weighting that focuses on adjustments to weight for nonresponse, frame deficiencies, and the effects of estimator instability An updated discussion of sample survey data analysis that includes analytic procedures for estimation and hypothesis testing A new section on Chromy's widely used method of taking probability proportional to size samples with minimum replacement of primary sampling units An expanded index with references on the latest research in the field
All of the book's examples and exercises can be easily worked out using various software packages including SAS, STATA, and SUDAAN, and an extensive FTP site contains additional data sets. With its comprehensive presentation and wealth of relevant examples, Sampling of Populations, Fourth Edition is an ideal book for courses on survey sampling at the upper-undergraduate and graduate levels. It is also a valuable reference for practicing statisticians who would like to refresh their knowledge of sampling techniques.
1,001 Statistics Practice Problems For Dummies takes you beyond the instruction and guidance offered in Statistics For Dummies to give you a more hands-on understanding of statistics. The practice problems offered range in difficulty, including detailed explanations and walk-throughs.
In this series, every step of every solution is shown with explanations and detailed narratives to help you solve each problem. With the book purchase, you’ll also get access to practice statistics problems online. This content features 1,001 practice problems presented in multiple choice format; on-the-go access from smart phones, computers, and tablets; customizable practice sets for self-directed study; practice problems categorized as easy, medium, or hard; and a one-year subscription with book purchase.Offers on-the-go access to practice statistics problems Gives you friendly, hands-on instruction 1,001 statistics practice problems that range in difficulty
1,001 Statistics Practice Problems For Dummies provides ample practice opportunities for students who may have taken statistics in high school and want to review the most important concepts as they gear up for a faster-paced college class.
One is heuristic and nonrigorous, and attempts to develop in students an intuitive feel for the subject that enables him or her to think probabilistically. The other approach attempts a rigorous development of probability by using the tools of measure theory. The first approach is employed in this text.
The book begins by introducing basic concepts of probability theory, such as the random variable, conditional probability, and conditional expectation. This is followed by discussions of stochastic processes, including Markov chains and Poison processes. The remaining chapters cover queuing, reliability theory, Brownian motion, and simulation. Many examples are worked out throughout the text, along with exercises to be solved by students.
This book will be particularly useful to those interested in learning how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. Ideally, this text would be used in a one-year course in probability models, or a one-semester course in introductory probability theory or a course in elementary stochastic processes.
New to this Edition:
65% new chapter material including coverage of finite capacity queues, insurance risk models and Markov chainsContains compulsory material for new Exam 3 of the Society of Actuaries containing several sections in the new examsUpdated data, and a list of commonly used notations and equations, a robust ancillary package, including a ISM, SSM, and test bankIncludes SPSS PASW Modeler and SAS JMP software packages which are widely used in the field
Superior writing styleExcellent exercises and examples covering the wide breadth of coverage of probability topics Real-world applications in engineering, science, business and economics
The author begins with basic characteristics of financial time series data before covering three main topics:Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods
Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets.
The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.
Focusing on the underlying structure of a system, Optimal Design of Queueing Systems explores how to set the parameters of a queueing system, such as arrival and service rates, before putting it into operation. It considers various objectives, comparing individually optimal (Nash equilibrium), socially optimal, class optimal, and facility optimal flow allocations.
After an introduction to basic design models, the book covers the optimal arrival rate model for a single-facility, single-class queue as well as dynamic algorithms for finding individually or socially optimal arrival rates and prices. It then examines several special cases of multiclass queues, presents models in which the service rate is a decision variable, and extends models and techniques to multifacility queueing systems. Focusing on networks of queues, the final chapters emphasize the qualitative properties of optimal solutions.
Written by a long-time, recognized researcher on models for the optimal design and control of queues and networks of queues, this book frames the issues in the general setting of a queueing system. It shows how design models can control flow to achieve a variety of objectives.
* Easy-to-follow format incorporates medical examples, step-by-step methods, and check yourself exercises
* Two-part design features course material and a professional reference section
* Chapter summaries provide a review of formulas, method algorithms, and check lists
* Companion site links to statistical databases that can be downloaded and used to perform the exercises from the book and practice statistical methods
New in this Edition:
* New chapters on: multifactor tests on means of continuous data, equivalence testing, and advanced methods
* New topics include: trial randomization, treatment ethics in medical research, imputation of missing data, and making evidence-based medical decisions
* Updated database coverage and additional exercises
* Expanded coverage of numbers needed to treat and to benefit, and regression analysis including stepwise regression and Cox regression
Thorough discussion on required sample size
"Seamless R and C++ integration with Rcpp" is simply a wonderful book. For anyone who uses C/C++ and R, it is an indispensable resource. The writing is outstanding. A huge bonus is the section on applications. This section covers the matrix packages Armadillo and Eigen and the GNU Scientific Library as well as RInside which enables you to use R inside C++. These applications are what most of us need to know to really do scientific programming with R and C++. I love this book. -- Robert McCulloch, University of Chicago Booth School of Business
Rcpp is now considered an essential package for anybody doing serious computational research using R. Dirk's book is an excellent companion and takes the reader from a gentle introduction to more advanced applications via numerous examples and efficiency enhancing gems. The book is packed with all you might have ever wanted to know about Rcpp, its cousins (RcppArmadillo, RcppEigen .etc.), modules, package development and sugar. Overall, this book is a must-have on your shelf. -- Sanjog Misra, UCLA Anderson School of Management
The Rcpp package represents a major leap forward for scientific computations with R. With very few lines of C++ code, one has R's data structures readily at hand for further computations in C++. Hence, high-level numerical programming can be made in C++ almost as easily as in R, but often with a substantial speed gain. Dirk is a crucial person in these developments, and his book takes the reader from the first fragile steps on to using the full Rcpp machinery. A very recommended book! -- Søren Højsgaard, Department of Mathematical Sciences, Aalborg University, Denmark
"Seamless R and C ++ Integration with Rcpp" provides the first comprehensive introduction to Rcpp. Rcpp has become the most widely-used language extension for R, and is deployed by over one-hundred different CRAN and BioConductor packages. Rcpp permits users to pass scalars, vectors, matrices, list or entire R objects back and forth between R and C++ with ease. This brings the depth of the R analysis framework together with the power, speed, and efficiency of C++.
Dirk Eddelbuettel has been a contributor to CRAN for over a decade and maintains around twenty packages. He is the Debian/Ubuntu maintainer for R and other quantitative software, edits the CRAN Task Views for Finance and High-Performance Computing, is a co-founder of the annual R/Finance conference, and an editor of the Journal of Statistical Software. He holds a Ph.D. in Mathematical Economics from EHESS (Paris), and works in Chicago as a Senior Quantitative Analyst.
The book adapts formalism across a number of disciplines to the strategy for design of mutilevel interventions, focusing first on molecular, cellular, and larger scale examples, and then extending the argument to the simplifications provided by the dominant role of social and cultural structures and processes in individual and population patterns of health and illness.
In place of “magic bullets”, we must now apply “magic strategies” that act across both the scale and level of organization. This book provides an introductory roadmap to the new tools that will be needed for the design of such strategies.Contents:Beyond Magic BulletsExpanding the TheoryDynamic ‘Regression Models’An Evolutionary ExcursionExample: Mental DisordersExample: Protein FoldingExample: Glycome DeterminantsExample: Glycan/Lectin Logic GatesExample: IDP Logic GatesTreatmentHistory and HealthBeyond GlasperlenspielMathematical Appendix
Readership: Undergraduate, graduate, researchers and professionals in biomathematics, biostatistics, mathematical modeling, complex systems and pharmaceuticals.
Keywords:Cognition;Information Theory;Mathematical Model;Social EpidemiologyKey Features:The book synthesizes published, peer-reviewed articles to make explicit the complexity of human biology that underlies the catastrophic failure of the pharmaceutical industryIt develops a formalism of statistical tools for modeling and data analysis to explicitly address that multiscale and multilevel complexityIt uses the formalism to explore case histories at a number of scales
The authors begin with an overview of signal processing and machine learning approaches and continue on to introduce specific applications, which illustrate CI’s importance in medical diagnosis and healthcare. They provide an extensive review of signal processing techniques commonly employed in the analysis of biomedical signals and in the improvement of signal to noise ratio. The text covers recent CI techniques for post processing ECG signals in the diagnosis of cardiovascular disease and as well as various studies with a particular focus on CI’s potential as a tool for gait diagnostics.
In addition to its detailed accounts of the most recent research, Computational Intelligence in Biomedical Engineering provides useful applications and information on the benefits of applying computation intelligence techniques to improve medical diagnostics.
At present, quantitative ecological risk assessment is widely used in different contexts, however very often without an understanding of the natural mechanisms that drive the processes of environmental and human risk. Its application is often accompanied by high uncertainty about risk values. On the other hand, the sustainability of modern technoecosystems is known because of their natural biogeochemical cycling that has been transformed to various extents by anthropogenic studies. Accordingly our understanding of the principal mechanisms that drive the biogeochemical food webs allows us to present a quantitative ecological risk assessment and to propose technological solutions for management of various ERA enterprises. It also enables us to devise a powerful mechanism for ecological insurance, to assign responsibilities and protect rights while managing the control of damage from natural and anthropogenic accidents and catastrophes.